Modulating the 0D/2D Interface of Hybrid Semiconductors for Enhanced Photoelectrochemical Performances
Bibliographic record
Abstract
Abstract Photoelectrochemical (PEC) solar‐driven hydrogen production is a promising route to convert solar energy into chemical energy using semiconductors as active materials. However, the performance is still far from satisfactory due to a limited absorption range and rapid charge recombination. Compared to 3D semiconductors, 0D/2D nanohybrids may exhibit better PEC performance, due to the formation of an intimate interface between the two semiconductors that can inhibit carrier recombination. Herein, a photoelectrode based on a 0D/2D heterojunction is constructed by 0D metal chalcogenide quantum dots (QDs) and hierarchical 2D Zn‐MoS2 nanosheets (NSs). The effect of PbS, CdS, and their composite PbS@CdS QDs is analyzed by depositing them onto Zn‐MoS2 NSs using an in situ process. This distinctive heterojunction can leverage the light harvesting capabilities of QDs with the catalytic performance of Zn‐MoS2. Compared to Zn‐MoS2, Zn‐MoS2/PbS, and Zn‐MoS2/CdS, the obtained 0D/2D heterostructure based on the composite Zn‐MoS2/PbS@CdS has a significantly enhanced photocurrent. The synergistic effect between 0D/2D heterojunction, the extended absorption range of QDs, and the strong coupling and band alignment between them lead to superior solar‐driven PEC performance. This work can provide a new platform to construct multifunctional 0D/2D nanohybrids for optoelectronic applications, not limited to PEC devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".